Text Generation
Transformers
Safetensors
llama
orpheus
text-to-speech
voice-cloning
speech-synthesis
snac
multilingual
low-resource
underrepresented-languages
unsloth
lora
qlora
continued-finetuning
hypa-ai
conversational
text-generation-inference
Instructions to use hypaai/Hypa-Orpheus-3b-TTS-VC-ext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hypaai/Hypa-Orpheus-3b-TTS-VC-ext with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="hypaai/Hypa-Orpheus-3b-TTS-VC-ext") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("hypaai/Hypa-Orpheus-3b-TTS-VC-ext") model = AutoModelForCausalLM.from_pretrained("hypaai/Hypa-Orpheus-3b-TTS-VC-ext", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use hypaai/Hypa-Orpheus-3b-TTS-VC-ext with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hypaai/Hypa-Orpheus-3b-TTS-VC-ext" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hypaai/Hypa-Orpheus-3b-TTS-VC-ext", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/hypaai/Hypa-Orpheus-3b-TTS-VC-ext
- SGLang
How to use hypaai/Hypa-Orpheus-3b-TTS-VC-ext with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "hypaai/Hypa-Orpheus-3b-TTS-VC-ext" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hypaai/Hypa-Orpheus-3b-TTS-VC-ext", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "hypaai/Hypa-Orpheus-3b-TTS-VC-ext" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hypaai/Hypa-Orpheus-3b-TTS-VC-ext", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Desktop
- Docker Model Runner
How to use hypaai/Hypa-Orpheus-3b-TTS-VC-ext with Docker Model Runner:
docker model run hf.co/hypaai/Hypa-Orpheus-3b-TTS-VC-ext
Download requirements.txt from hypaai/Hypa-Orpheus-3b-TTS-VC-ext: direct link, hf CLI and curl.
- Browser
- Download file 232 Bytes
-
https://huggingface.co/hypaai/Hypa-Orpheus-3b-TTS-VC-ext/resolve/main/requirements.txt
- Command line
-
hf download hf://hypaai/Hypa-Orpheus-3b-TTS-VC-ext/requirements.txt
-
curl -L -o requirements.txt https://huggingface.co/hypaai/Hypa-Orpheus-3b-TTS-VC-ext/resolve/main/requirements.txt
232 Bytes
| --extra-index-url https://download.pytorch.org/whl/cu121 | |
| torch==2.4.0+cu121 | |
| torchaudio==2.4.0+cu121 | |
| transformers==4.45.2 | |
| accelerate==0.34.2 | |
| numpy==1.26.4 | |
| protobuf==4.25.3 | |
| snac==1.2.1 | |
| diffusers==0.30.3 | |
| vllm==0.6.3.post1 | |
| starlette<1.0 |